一种基于自适应种群变异鸽群优化的航天器集群轨道规划方法

Spacecraft cluster orbit planning method based on adaptive population mutated pigeon group optimization

航天器集群在复杂条件下的轨道规划问题是当前航天领域的热点以及难点.本文针对分布式集群航天器在队形变换过程中的轨道最优规划问题进行了研究,提出了基于自适应种群变异的鸽群算法(adaptive population variation pigeon-inspired optimization,APVPIO).本文对经典PIO算法中的核心演化算法、演化停滞以及易陷入局部最优解问题进行了研究.同时针对经典PIO算法的适应度函数进行了研究,并且结合轨道规划问题进行了改进.最后基于自适应种群变异的鸽群算法进行了仿真实验,结果表明,APVPIO算法,相比于经典PIO算法、PSO算法在极大减少计算量的同时,有更优规划结果、更深的种群演化深度以及更快的收敛速度,可以满足航天器集群在复杂约束条件下的轨道规划问题.

The orbit planning problem of spacecraft clusters under complex conditions is a hotspot and a difficult goal in the current aerospace field.This paper studies the orbital optimal programming problem of distributed cluster spacecraft in the process of formation transformation and proposes the population evolution algorithm adaptive population variation pigeon-inspired optimization(APVPIO).Based on the core evolutionary algorithm and evolutionary stagnation,there is a high tendency to fall into the local optimal solution in the classical PIO algorithm.The fitness function of the classical PIO algorithm is studied and improved with the orbit planning problem.Finally,the simulation based on the adaptive population variation algorithm is performed.The results reveal that the APVPIO algorithm has better planning results,deeper population evolution depth,and a faster convergence speed in comparison to the classical PIO algorithm and particle swarm algorithm(PSO)algorithm.Hence,it has the potential to meet the complexity requirement of spacecraft clusters and orbital planning problems.